March 18, 2024, 7:34 p.m. | clemra

DEV Community dev.to




Why you need observability for RAG


There are so many different ways to make RAG work for a use case. What vector store to use? What retrieval strategy to use? LlamaIndex makes it easy to try many of them without having to deal with the complexity of integrations, prompts and memory all at once.


Initially, we at Langfuse worked on complex RAG/agent applications and quickly realized that there is a new need for observability and experimentation to tweak and iterate …

case code complexity deal easy index integrations llama llamaindex observability opensource rag retrieval store strategy them vector vector store work

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

AIML - Sr Machine Learning Engineer, Data and ML Innovation

@ Apple | Seattle, WA, United States

Senior Data Engineer

@ Palta | Palta Cyprus, Palta Warsaw, Palta remote